Search: “papers”

Table-R1: Region-based Reinforcement Learning for Table Understanding

model-releasesarxiv-cs-ai

arXiv:2505.12415v3 Announce Type: replace-cross Abstract: Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for

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Teaching and Learning under Deductive Errors

DGX agent

arXiv:2605.13384v1 Announce Type: new Abstract: Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language

researcharxiv-cs-lg

14 May 2026

TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior

arXiv:2602.09628v2 Announce Type: replace Abstract: Real-time whole-body teleoperation is a critical method for humanoid robots to perform complex tasks in unstructured environments. However, developi

safetyarxiv-cs-ro

The Joint Gromov Wasserstein Objective for Multiple Object Matching

arXiv:2511.16868v2 Announce Type: replace Abstract: The Gromov-Wasserstein (GW) distance serves as a powerful tool for matching objects in metric spaces. However, its traditional formulation is constr

applicationsarxiv-cs-cv

The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support

arXiv:2605.12547v1 Announce Type: cross Abstract: Public procurement is vulnerable to error, fraud and corruption, yet high transaction volumes overwhelm oversight. While research often focuses on ten

The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy

arXiv:2605.12735v1 Announce Type: new Abstract: We introduce and open-source the Unified Autonomy Stack, a system-level solution that enables resilient autonomy across diverse aerial and ground robot

safetyarxiv-cs-ro

ThermalTap: Passive Application Fingerprinting in VR Headsets via Thermal Side Channels

arXiv:2605.12927v1 Announce Type: cross Abstract: Standalone virtual reality (VR) headsets process highly sensitive personal, professional, and health-related data, yet their susceptibility to non-con

applicationsarxiv-cs-cv

Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting

arXiv:2605.13678v1 Announce Type: new Abstract: Recent studies on long-term time series forecasting have shown that simple linear models and MLP-based predictors can achieve strong performance without

Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

arXiv:2402.05576v4 Announce Type: replace Abstract: Machine learning models with inputs in a Euclidean space R^d, when implemented on digital computers, generalize, and their generalization gap conver

ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery

arXiv:2605.12784v1 Announce Type: new Abstract: Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approa

agentsarxiv-cs-lg

Towards a holistic understanding of Selection Bias for Causal Effect Identification

arXiv:2605.13430v1 Announce Type: cross Abstract: Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents a

safetyarxiv-cs-ai

TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation

arXiv:2605.13433v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-

U-HNO: A U-shaped Hybrid Neural Operator with Sparse-Point Adaptive Routing for Non-stationary PDE Dynamics

arXiv:2605.12965v1 Announce Type: new Abstract: Solutions to many partial differential equations (PDEs) display coexisting smooth global transport and localized sharp features within a single trajecto

Uncertainty-Aware 3D Position Refinement for Multi-UAV Systems

arXiv:2605.13500v1 Announce Type: new Abstract: Reliable real-time 3D localization is essential for multi-UAV navigation, collision avoidance, and coordinated flight, yet onboard estimates can degrade

agentsarxiv-cs-ro

Unified generalization analysis for physics informed neural networks

arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use

Tutorials

VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning

arXiv:2504.11944v3 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications wher

tutorialsarxiv-cs-ai

What Happens Before Decoding? Prefill Determines GUI Grounding in VLMs

arXiv:2605.12549v1 Announce Type: new Abstract: Existing training-free approaches for GUI grounding often rely on multiple inference runs, such as iterative cropping or candidate aggregation, to ident

What Limits Vision-and-Language Navigation ?

arXiv:2605.13328v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degr

agentsarxiv-cs-ai

When Diffusion Breaks Constraints: Sequential Autoregressive Generation with RL and MCTS

arXiv:2512.01242v3 Announce Type: replace-cross Abstract: Data-driven generative models excel in language and vision, but diffusion models often fail in constrained planning and design tasks, exhibiti

researcharxiv-cs-ai

3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark

model-releasesarxiv-cs-cv

arXiv:2605.12437v1 Announce Type: new Abstract: Retrospective novel view synthesis (NVS) of dynamic scenes is fundamental to applications such as sports. Recent dynamic 3D Gaussian Splatting (3DGS) ap

4DVGGT-D: 4D Visual Geometry Transformer with Improved Dynamic Depth Estimation

arXiv:2605.12027v1 Announce Type: new Abstract: Reconstructing dynamic 4D scenes from monocular videos is a fundamental yet challenging task. While recent 3D foundation models provide strong geometric

A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length

arXiv:2605.11406v1 Announce Type: new Abstract: Existing granular-ball classification methods are often driven by handcrafted quality measures, neighborhood rules, or heuristic splitting and stopping

A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

arXiv:2605.12328v1 Announce Type: new Abstract: Data entry systems remain structurally vulnerable to categorical misclassifications, particularly in small and medium sized enterprises (SMEs). When nom

researcharxiv-cs-cl

A Comparative Study of Controlled Text Generation Systems Using Level-Playing-Field Evaluation Principles

arXiv:2605.12395v1 Announce Type: new Abstract: Background: Many different approaches to controlled text generation (CTG) have been proposed over recent years, but it is difficult to get a clear pictu

researcharxiv-cs-cl

A Composite Activation Function for Learning Stable Binary Representations

arXiv:2605.11558v1 Announce Type: new Abstract: Activation functions play a central role in neural networks by shaping internal representations. Recently, learning binary activation representations ha

A comprehensive control architecture for semi-autonomous dual-arm robots in agriculture settings

arXiv:2506.23723v2 Announce Type: replace Abstract: The adoption of mobile robotic platforms in complex environments, such as agricultural settings, requires these systems to exhibit a flexible yet ef

agentsarxiv-cs-ro

A New Technique for AI Explainability using Feature Association Map

arXiv:2605.12350v1 Announce Type: new Abstract: Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI sys

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design

arXiv:2605.11759v1 Announce Type: cross Abstract: Dimensionality reduction is essential in simulation-based shape design, where high-dimensional parameterizations hinder optimization, surrogate modeli

A Semi-Supervised Framework for Speech Confidence Detection using Whisper

arXiv:2605.12387v1 Announce Type: cross Abstract: Automatic detection of speaker confidence is critical for adaptive computing but remains constrained by limited labelled data and the subjectivity of

Tutorials

A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by

tutorialsarxiv-cs-cv

Active inference as a unified model of collision avoidance behavior in human drivers

arXiv:2506.02215v5 Announce Type: replace Abstract: Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving

researcharxiv-cs-ro

AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents

arXiv:2605.11026v1 Announce Type: cross Abstract: Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks

agentsarxiv-cs-cl

Industry

Alexa is moving into Amazon․com

Amazon is bringing Alexa Plus to Amazon.com, integrating its LLM-powered AI assistant directly into the company's shopping experience. Beginning today, when you type a query into Amazon, you'll be tal

industrythe-verge-ai

Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information

arXiv:2505.17907v2 Announce Type: replace-cross Abstract: Properties of Fisher information matrices of 2-layer neural ReLU networks with random hidden weights are studied. For these networks, it is kn

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System

arXiv:2605.12025v1 Announce Type: new Abstract: Neural operators provide a framework for learning solution operators of partial differential equations (PDEs), enabling efficient surrogate modeling for

Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy

arXiv:2412.11875v3 Announce Type: replace-cross Abstract: Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inver

applicationsarxiv-cs-lg

Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification

arXiv:2605.11460v1 Announce Type: new Abstract: System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlin

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models

arXiv:2605.11726v1 Announce Type: new Abstract: Recently, reinforcement learning (RL) has been widely applied during post-training for diffusion large language models (dLLMs) to enhance reasoning with

Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters

model-releasesarxiv-cs-cv

arXiv:2605.11960v1 Announce Type: new Abstract: Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner

arXiv:2510.03206v2 Announce Type: replace-cross Abstract: Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical

applicationsarxiv-cs-cl

Combining On-Policy Optimization and Distillation for Long-Context Reasoning in Large Language Models

arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E

safetyarxiv-cs-cl

Compositional Neural Operators for Multi-Dimensional Fluid Dynamics

arXiv:2605.11691v1 Announce Type: new Abstract: Partial differential equations (PDEs) govern diverse physical phenomena, yet high-fidelity numerical solutions are computationally expensive and Machine

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification

arXiv:2509.20899v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but exte

Control Charts for Multi-agent Systems

arXiv:2605.11135v1 Announce Type: cross Abstract: Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal

agentsarxiv-cs-lg

Control of Fully Actuated Aerial Vehicles: A Comparison of Model-based and Sensor-based Dynamic Inversion

model-releasesarxiv-cs-ro

arXiv:2605.12071v1 Announce Type: new Abstract: Fully actuated multirotor platforms decouple translational force generation from vehicle attitude, enabling independent control of position and orientat

DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift

arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-

Diabetic Retinopathy Classification using Downscaling Algorithms and Deep Learning

arXiv:2605.11430v1 Announce Type: new Abstract: Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with clas

Local Ai

Distributed Quantum Gaussian Processes for Multi-Agent Systems

arXiv:2602.15006v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale re

local-aiarxiv-cs-lg